July 17th, 2023: Topic – Large Language Models / LLMs& NLP
| Title | Speaker | Time | Video/PPT Link (if available) | Sand Number(s) (If Applicable) |
|---|---|---|---|---|
| DAY 1, SESSION 1 – (Topic: Large Language Models / LLMs & NLP) | ||||
| Keynote #1: Panel discussion on LLMs | Scott Steinmetz (SNL) | 9:10am (MST) | Video | SAND2023-05938C |
| Advancements in Machine Learning | Christopher Symonds (SNL) | 10:30am (MST) | Video / PPT | SAND2023-06092V, SAND2023-03537C |
| Unlocking the Power of Large Language Models: Practical Applications in Scripts and Programs | Anthony Garland (SNL) | 11:05am (MST) | Video / PPT | SAND2023-06223C, SAND2023-06426V |
| The Cost of Custom Large Language Model (LLM) Solutions | Henry Wong (SNL) | 11:25am (MST) | Video / PPT | SAND2023-06182V, SAND2023-06132PE |
| Hierarchical partition of unity networks: fast multilevel training | Nat Trask (SNL) | 11:45am (MST) | Video | NA |
| AI-enhanced simulation of operational requirements management with a knowledge graph-based digital twin | Matthew T. Dearing (ANL) | 1:00pm (MST) | Video / PPT | NA |
| ASPER: ASP-enhanced Deep Learning Models for Joint Entity-Relation Extraction | Trung Le (New Mexico State University) | 1:20pm (MST) | Video / PPT | NA |
July 18th, 2023: Topics – Climate Science, Physics Informed Machine Learning and Nuclear Science
| Title | Speaker | Time | Video/PPT Link (if available) | SAND Number(s) (If Applicable) |
|---|---|---|---|---|
| DAY 2, SESSION 1 – (Topic: Climate Science) | ||||
| Keynote #2: Selected Machine Learning Applications in Weather and Climate | Paul Roebber (University of Wisconsin) | 9:05am (MST) | Video / PPT | NA |
| Block Dynamic Mode Decomposition: Domain-Aware Machine Learning for Climate Modelling and Feedback Analysis | Craig Bakker (PNNL) | 9:35am (MST) | Video / PPT | NA |
| 2022 Featured Talk "Testing the Impact of Specificity on Human Interpretations of State Uncertainty" | Laura Matzen (SNL) | 9:50am (MST) | Video / PPT | SAND2022-9130 O, SAND2022-9746 V |
| Characterizing climate pathways using feature importance on echo state networks | Katherine Goode (SNL) | 10:15am (MST) | Video | SAND2023-05920C, SAND2023-06127C |
| Solving High-Dimensional Inverse Problems with Auxiliary Uncertainty via Operator Learning with Limited Data | Mamikon Gulian (SNL) | 10:35am (MST) | Video / PPT | SAND2023-06014C, SAND2023-02512C |
| DAY 2, SESSION 2 – (Topic: Physics-Informed Machine Learning) | ||||
| Data-Driven Structure Preservation for Scientific Machine Learning | Jonas Actor (SNL) | 12:00pm (MST) | Video / PPT | SAND2023-04587C, SAND2023-06028O |
| Spatio-Temporal Super-Resolution of Dynamical Systems using Physics-Informed Deep-Learning | Ankit Shrivastava (SNL) | 12:20pm (MST) | Video / PPT | SAND2023-06008C, SAND2023-06215C |
| Manifold Learning & Equations of State: Conservation of Energy as Regularization for Neural Networks | George A. Kevrekidis (LANL) | 12:35pm (MST) | Video / PPT | NA |
| Multiscale Damage via Physics-Informed Recurrent Neural Networks | Ramin Bostanabad (UC Irvine) | 1:00pm (MST) | Video / PPT | NA |
| Utilizing Physics-informed and Machine Learning Methods to Enhance Remote Monitoring of Physiological Signatures | Christopher Katinas (SNL) | 1:20pm (MST) | Video | SAND2023-05771 C |
| Closure modeling through the lens of multifidelity operator learning | Shady Ahmed (PNNL) | 1:40pm (MST) | Video / PPT | NA |
| DAY 2, SESSION 3 – (Topic: Nuclear Science) | ||||
| Machine Learning Surrogates for Time Dependent Fuel Degradation Processes in Nuclear Waste Repository Simulations | Bert Debusschere (SNL) | 2:15pm (MST) | Video / PPT | SAND2023-05874C, SAND2023-05942C |
| Transferable and explainable reactor power level models using nonradiological data | Jon Whetzel (SNL) | 2:35pm (MST) | Video / PPT | SAND2023-04195C, SAND2023-06322O |
| A Semi-Supervised Model for Multi-Label Radioisotope Classification and Out-of-Distribution Detection | Alan Van Omen (SNL) | 2:50pm (MST) | Video / PPT | SAND2023-01160PE, SAND2023-05077V |
July 19th, 2023: Topics – All Things Data, Material Science & Manufacturing
| Title | Speaker | Time | Video/PPT Link (if available) | SAND Number(s) (If Applicable) |
|---|---|---|---|---|
| DAY 3, SESSION 1 – (Topic: All Things Data) | ||||
| Keynote #3: Want To Do AI? Bring a Large Toolbox! | George Luger (UNM) | 9:05am (MST) | Video | NA |
| Autoencoders for Multimodal Data Fusion | Georgios Fragkos (SNL) | 9:35am (MST) | Video | SAND2023-05923V |
| Leveraging 3D Game Engines for High-Quality Synthetic Data in Object Detection Applications | Lucas Zhou (SNL) | 9:50am (MST) | Video / PPT | SAND2023-06188C, SAND2023-06189V |
| Synthetic data generation to improve object detection in x-ray radiographs | Heidi Komkov (SNL) | 10:05am (MST) | Video / PPT | SAND2023-02778C, SAND2023-06303C |
| Turtle: A Tool for Catching Subpopulation Trends in Performance Data | Skyler Gray (SNL) | 10:25am (MST) | Video | SAND2023-06388A, SAND2023-06436V |
| Probabilistic Neural Data Fusion for Learning from an Arbitrary Number of Multi-fidelity Data Sets | Carlos Mora Sardiña (UC Irvine) | 10:40am (MST) | Video / PPT | NA |
| Quantifying the Sensitivity of Magnetohydrodynamics Simulations of Pulsed-Power Experiments Using Multi-Fidelity Electrical Conductivity Data | Lucas Stanek (SNL) | 11:00am (MST) | Video / PPT | SAND2023-05454C, SAND2023-06578V |
| Battery Cycle Life Prediction Across a Variety of Datasets | Joseph Lubars (SNL) | 11:20am (MST) | Video | SAND2023-06588C, SAND2023-06130C |
| DAY 3, SESSION 2 – (Topic: Material Science & Manufacturing) | ||||
| Multi-Fidelity Bayesian Optimization for Materials Design | Zahra Zanjani Foumani (UC Irvine) | 1:00pm (MST) | Video / PPT | NA |
| PRISM: Process Parameter Optimization for Selective Manufacturing | Anthony Garland (SNL) | 1:20pm (MST) | Video | SAND2023-06202C, SAND2023-06567V |
| Adaptive spatiotemporal dimension reduction in concurrent multiscale damage analysis | Shiguang Deng (UC Irvine) | 1:35pm (MST) | Video | NA |
| Deep Reinforcement Learning for the Design of Mechanical Metamaterials with Customizable Nonlinear Deformation Responses | Nathan Brown (SNL) | 2:00pm (MST) | Video | SAND2023-06242C, SAND2023-06243V |
| Hydrodynamic parameter estimation using statistical machine learning for dynamic radiography | Soumi De (LANL) | 2:20pm (MST) | Video / PPT | NA |
| Artificial Intelligence and Machine Learning Support for Probabilistic Fracture Mechanics Analysis | Stephen Verzi (SNL) | 2:40pm (MST) | Video / PPT | SAND2023-06170PE, SAND2023-06262V |
| Multivariate Analysis as a Tool for Validating Tester Matching | Rosalie Multari (SNL) | 3:00pm (MST) | Video | SAND2023-05968C, SAND2023-06472C |
July 20th, 2023: Topics – Theory & Applications
| Title | Speaker | Time | Video/PPT Link (if available) | SAND Number(s) (If Applicable) |
|---|---|---|---|---|
| DAY 4, SESSION 1 – (Topic: Theory & Applications) | ||||
| Keynote #4: Deep learning estimation of modified Zernike coefficients for image point spread functions | Steven Sandoval (New Mexico State University) | 9:05am (MST) | Video / PPT | NA |
| Trusted AI to Aid HPC Software Development for U.S. DoE’s Scientific Software | Vivek Kale (SNL) | 9:35am (MST) | PPT (with audio) | SAND2023-06348A, SAND2023-06402O |
| Machine Learning and Autonomous Decision Making for National Security: Designing Decision Rules using Multiobjective Optimization | Mark A. Smith (SNL) | 9:55am (MST) | Video | SAND2023-05973C |
| 2022 Highlight: Widget Feature Analysis: A Tale of Two Features | Sarah Ackerman (SNL) | 10:15am (MST) | Video / PPT | SAND2022-10076 PE, SAND2022-10075 V |
| Real-time decision-making support for risk-reduction during severe accidents | Alex Washburne (SNL) | 10:35am (MST) | Video | SAND2023-06524C |
| AI-enabled conflict simulation | Ephraim Rusu (LLNL) | 10:50am (MST) | Video / PPT | NA |
| Sandia Analysis Workbench (SAW) Workflow using Xyce-PyMi for Simulating Circuit-Level Response using Data Driven Device Models | Paul Kuberry (SNL) | 11:05am (MST) | Video | SAND2023-06530O, SAND2023-04220C |
| Learning Wildfire Dynamics for Ignition Inversion | Zachary Morrow (SNL) | 11:35am (MST) | Video / PPT | SAND2023-06118PE, SAND2023-06373V |
| DAY 4, SESSION 2 – (Topic: Theory & Applications) | ||||
| Applying Machine Learning in the Characterization of Ship Tracks | Pierce Warburton (SNL) | 1:00pm (MST) | Video | SAND2023-06305PE, SAND2023-06455V |
| Behavioral Segmentation and Clustering of Geospatial Trajectories | Jessica Jones (SNL) | 1:15pm (MST) | Video / PPT | SAND2023-06025C |
| Tracking Walking Droplets | Erdi Kara (Spelman College) | 1:35pm (MST) | Video | NA |
| Statistical modeling for detecting anomalous event timings on host-based cybersecurity log data | Alexander Foss (SNL) | 1:55pm (MST) | PPT (with audio) | SAND2023-05908C, SAND2023-06444 |
| Deep Offline Reinforcement Learning with Recurrent Training for Cyberphysical Threat Detection | Georgios Fragkos (SNL) | 2:20pm (MST) | Video | SAND2023-05927V |
| Sensor and Actuator Attacks on Hierarchical Control Systems with Domain-Aware Operator Theory | Craig Bakker (PNNL) | 2:40pm (MST) | Video / PPT | NA |
| Poisson Response Tensor-on-Tensor Regression | Carlos Llosa (SNL) | 2:55pm (MST) | Video / PPT | SAND2023-05913V, SAND2023-05481C |
| Class-Specific Attention (CSA) for Classifying Time-Series Data | Huiping Cao (New Mexico State University) | 3:15pm (MST) | Video / PPT | NA |